Overview

Dataset statistics

Number of variables24
Number of observations97065
Missing cells0
Missing cells (%)0.0%
Duplicate rows2980
Duplicate rows (%)3.1%
Total size in memory16.1 MiB
Average record size in memory174.0 B

Variable types

Boolean2
Numeric10
Categorical12

Alerts

x3_Mozilla/5.0 has constant value "1.0" Constant
x4_KHTML/3.5.8 (like Gecko) has constant value "1.0" Constant
x5_HTTP/1.1 has constant value "1.0" Constant
x6_compatible; Konqueror/3.5; Linux has constant value "1.0" Constant
Dataset has 2980 (3.1%) duplicate rowsDuplicates
Content-Length is highly correlated with num_of_args and 5 other fieldsHigh correlation
num_of_args is highly correlated with Content-Length and 8 other fieldsHigh correlation
max_length_of_args is highly correlated with num_of_args and 4 other fieldsHigh correlation
min_length_of_args is highly correlated with num_of_args and 6 other fieldsHigh correlation
total_length_args is highly correlated with Content-Length and 5 other fieldsHigh correlation
total_length_request is highly correlated with max_length_of_args and 3 other fieldsHigh correlation
lenght_of_path is highly correlated with num_of_pathsHigh correlation
port_is_common is highly correlated with x0_PUT and 2 other fieldsHigh correlation
num_of_paths is highly correlated with lenght_of_pathHigh correlation
num_sql_words is highly correlated with total_length_requestHigh correlation
num_special_chars is highly correlated with num_of_args and 4 other fieldsHigh correlation
x0_GET is highly correlated with Content-Length and 5 other fieldsHigh correlation
x0_POST is highly correlated with Content-Length and 4 other fieldsHigh correlation
x0_PUT is highly correlated with port_is_common and 2 other fieldsHigh correlation
x1_localhost:8080 is highly correlated with port_is_common and 2 other fieldsHigh correlation
x1_localhost:9090 is highly correlated with port_is_common and 2 other fieldsHigh correlation
x2_application/x-www-form-urlencoded is highly correlated with Content-Length and 5 other fieldsHigh correlation
x2_nan is highly correlated with Content-Length and 5 other fieldsHigh correlation
Content-Length is highly correlated with num_of_args and 5 other fieldsHigh correlation
num_of_args is highly correlated with Content-Length and 3 other fieldsHigh correlation
max_length_of_args is highly correlated with total_length_args and 1 other fieldsHigh correlation
total_length_args is highly correlated with Content-Length and 4 other fieldsHigh correlation
total_length_request is highly correlated with num_of_args and 3 other fieldsHigh correlation
lenght_of_path is highly correlated with num_of_pathsHigh correlation
port_is_common is highly correlated with x0_PUT and 2 other fieldsHigh correlation
num_of_paths is highly correlated with lenght_of_pathHigh correlation
num_sql_words is highly correlated with total_length_requestHigh correlation
num_special_chars is highly correlated with num_of_args and 3 other fieldsHigh correlation
x0_GET is highly correlated with Content-Length and 3 other fieldsHigh correlation
x0_POST is highly correlated with Content-Length and 3 other fieldsHigh correlation
x0_PUT is highly correlated with port_is_common and 2 other fieldsHigh correlation
x1_localhost:8080 is highly correlated with port_is_common and 2 other fieldsHigh correlation
x1_localhost:9090 is highly correlated with port_is_common and 2 other fieldsHigh correlation
x2_application/x-www-form-urlencoded is highly correlated with Content-Length and 3 other fieldsHigh correlation
x2_nan is highly correlated with Content-Length and 3 other fieldsHigh correlation
Content-Length is highly correlated with num_of_args and 5 other fieldsHigh correlation
num_of_args is highly correlated with Content-Length and 4 other fieldsHigh correlation
max_length_of_args is highly correlated with num_of_args and 3 other fieldsHigh correlation
min_length_of_args is highly correlated with num_of_args and 3 other fieldsHigh correlation
total_length_args is highly correlated with Content-Length and 4 other fieldsHigh correlation
total_length_request is highly correlated with num_sql_wordsHigh correlation
port_is_common is highly correlated with x0_PUT and 2 other fieldsHigh correlation
num_sql_words is highly correlated with total_length_requestHigh correlation
num_special_chars is highly correlated with num_of_args and 3 other fieldsHigh correlation
x0_GET is highly correlated with Content-Length and 3 other fieldsHigh correlation
x0_POST is highly correlated with Content-Length and 3 other fieldsHigh correlation
x0_PUT is highly correlated with port_is_common and 2 other fieldsHigh correlation
x1_localhost:8080 is highly correlated with port_is_common and 2 other fieldsHigh correlation
x1_localhost:9090 is highly correlated with port_is_common and 2 other fieldsHigh correlation
x2_application/x-www-form-urlencoded is highly correlated with Content-Length and 3 other fieldsHigh correlation
x2_nan is highly correlated with Content-Length and 3 other fieldsHigh correlation
x0_PUT is highly correlated with port_is_common and 6 other fieldsHigh correlation
port_is_common is highly correlated with x0_PUT and 6 other fieldsHigh correlation
x3_Mozilla/5.0 is highly correlated with x0_PUT and 12 other fieldsHigh correlation
x5_HTTP/1.1 is highly correlated with x0_PUT and 12 other fieldsHigh correlation
x0_POST is highly correlated with x3_Mozilla/5.0 and 6 other fieldsHigh correlation
x2_nan is highly correlated with x3_Mozilla/5.0 and 6 other fieldsHigh correlation
anomalous is highly correlated with x3_Mozilla/5.0 and 3 other fieldsHigh correlation
x0_GET is highly correlated with x3_Mozilla/5.0 and 6 other fieldsHigh correlation
x2_application/x-www-form-urlencoded is highly correlated with x3_Mozilla/5.0 and 6 other fieldsHigh correlation
num_batch_words is highly correlated with x3_Mozilla/5.0 and 3 other fieldsHigh correlation
x4_KHTML/3.5.8 (like Gecko) is highly correlated with x0_PUT and 12 other fieldsHigh correlation
x6_compatible; Konqueror/3.5; Linux is highly correlated with x0_PUT and 12 other fieldsHigh correlation
x1_localhost:9090 is highly correlated with x0_PUT and 6 other fieldsHigh correlation
x1_localhost:8080 is highly correlated with x0_PUT and 6 other fieldsHigh correlation
Content-Length is highly correlated with num_of_args and 3 other fieldsHigh correlation
num_of_args is highly correlated with Content-Length and 10 other fieldsHigh correlation
max_length_of_args is highly correlated with Content-Length and 3 other fieldsHigh correlation
min_length_of_args is highly correlated with num_of_args and 1 other fieldsHigh correlation
total_length_args is highly correlated with Content-Length and 4 other fieldsHigh correlation
total_length_request is highly correlated with num_of_args and 4 other fieldsHigh correlation
lenght_of_path is highly correlated with num_of_args and 1 other fieldsHigh correlation
port_is_common is highly correlated with x0_PUT and 2 other fieldsHigh correlation
num_of_paths is highly correlated with min_length_of_args and 1 other fieldsHigh correlation
num_sql_words is highly correlated with num_of_args and 2 other fieldsHigh correlation
num_special_chars is highly correlated with Content-Length and 5 other fieldsHigh correlation
x0_GET is highly correlated with num_of_args and 3 other fieldsHigh correlation
x0_POST is highly correlated with num_of_args and 3 other fieldsHigh correlation
x0_PUT is highly correlated with port_is_common and 2 other fieldsHigh correlation
x1_localhost:8080 is highly correlated with port_is_common and 2 other fieldsHigh correlation
x1_localhost:9090 is highly correlated with port_is_common and 2 other fieldsHigh correlation
x2_application/x-www-form-urlencoded is highly correlated with num_of_args and 3 other fieldsHigh correlation
x2_nan is highly correlated with num_of_args and 3 other fieldsHigh correlation
Content-Length has 71088 (73.2%) zeros Zeros
num_of_args has 45479 (46.9%) zeros Zeros
max_length_of_args has 45479 (46.9%) zeros Zeros
min_length_of_args has 45479 (46.9%) zeros Zeros
total_length_args has 45479 (46.9%) zeros Zeros
num_sql_words has 73275 (75.5%) zeros Zeros
num_special_chars has 51051 (52.6%) zeros Zeros

Reproduction

Analysis started2021-12-14 23:12:46.285503
Analysis finished2021-12-14 23:13:59.685501
Duration1 minute and 13.4 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

anomalous
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size94.9 KiB
False
72000 
True
25065 
ValueCountFrequency (%)
False72000
74.2%
True25065
 
25.8%
2021-12-14T18:13:59.816504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Content-Length
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct383
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.64981198
Minimum0
Maximum836
Zeros71088
Zeros (%)73.2%
Negative0
Negative (%)0.0%
Memory size379.3 KiB
2021-12-14T18:14:00.007504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile248
Maximum836
Range836
Interquartile range (IQR)4

Descriptive statistics

Standard deviation70.20024723
Coefficient of variation (CV)2.538905048
Kurtosis11.54919941
Mean27.64981198
Median Absolute Deviation (MAD)0
Skewness3.197737381
Sum2683829
Variance4928.074711
MonotonicityNot monotonic
2021-12-14T18:14:00.522501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
071088
73.2%
42057
 
2.1%
332054
 
2.1%
172046
 
2.1%
43990
 
1.0%
38965
 
1.0%
67612
 
0.6%
74566
 
0.6%
71504
 
0.5%
34460
 
0.5%
Other values (373)15723
 
16.2%
ValueCountFrequency (%)
071088
73.2%
42057
 
2.1%
5458
 
0.5%
639
 
< 0.1%
718
 
< 0.1%
874
 
0.1%
937
 
< 0.1%
1237
 
< 0.1%
137
 
< 0.1%
1435
 
< 0.1%
ValueCountFrequency (%)
8361
< 0.1%
8351
< 0.1%
8261
< 0.1%
8242
< 0.1%
8221
< 0.1%
8201
< 0.1%
8192
< 0.1%
8151
< 0.1%
8021
< 0.1%
7991
< 0.1%

num_of_args
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.812599804
Minimum0
Maximum26
Zeros45479
Zeros (%)46.9%
Negative0
Negative (%)0.0%
Memory size758.4 KiB
2021-12-14T18:14:00.761498image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q310
95-th percentile26
Maximum26
Range26
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.823530216
Coefficient of variation (CV)1.518000639
Kurtosis0.9282909415
Mean5.812599804
Median Absolute Deviation (MAD)2
Skewness1.538797344
Sum564200
Variance77.85468547
MonotonicityNot monotonic
2021-12-14T18:14:00.937505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
045479
46.9%
217693
 
18.2%
1013604
 
14.0%
2613555
 
14.0%
66722
 
6.9%
112
 
< 0.1%
ValueCountFrequency (%)
045479
46.9%
112
 
< 0.1%
217693
 
18.2%
66722
 
6.9%
1013604
 
14.0%
2613555
 
14.0%
ValueCountFrequency (%)
2613555
 
14.0%
1013604
 
14.0%
66722
 
6.9%
217693
 
18.2%
112
 
< 0.1%
045479
46.9%

max_length_of_args
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct165
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.75353629
Minimum0
Maximum597
Zeros45479
Zeros (%)46.9%
Negative0
Negative (%)0.0%
Memory size758.4 KiB
2021-12-14T18:14:01.182504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q324
95-th percentile44
Maximum597
Range597
Interquartile range (IQR)24

Descriptive statistics

Standard deviation29.8793528
Coefficient of variation (CV)2.025233287
Kurtosis198.8128948
Mean14.75353629
Median Absolute Deviation (MAD)8
Skewness11.38734443
Sum1432052
Variance892.7757237
MonotonicityNot monotonic
2021-12-14T18:14:01.449503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
045479
46.9%
234177
 
4.3%
144040
 
4.2%
283916
 
4.0%
193211
 
3.3%
333137
 
3.2%
243079
 
3.2%
152918
 
3.0%
102874
 
3.0%
22064
 
2.1%
Other values (155)22170
22.8%
ValueCountFrequency (%)
045479
46.9%
22064
 
2.1%
3490
 
0.5%
418
 
< 0.1%
574
 
0.1%
637
 
< 0.1%
81165
 
1.2%
91775
 
1.8%
102874
 
3.0%
11339
 
0.3%
ValueCountFrequency (%)
5975
< 0.1%
5887
< 0.1%
5875
< 0.1%
5822
 
< 0.1%
5812
 
< 0.1%
5802
 
< 0.1%
5791
 
< 0.1%
5787
< 0.1%
5762
 
< 0.1%
5746
< 0.1%

min_length_of_args
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.35844022
Minimum0
Maximum19
Zeros45479
Zeros (%)46.9%
Negative0
Negative (%)0.0%
Memory size758.4 KiB
2021-12-14T18:14:01.719497image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile8
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.966727343
Coefficient of variation (CV)1.447783504
Kurtosis6.296381141
Mean1.35844022
Median Absolute Deviation (MAD)1
Skewness2.412116698
Sum131857
Variance3.868016441
MonotonicityNot monotonic
2021-12-14T18:14:01.893503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
045479
46.9%
235008
36.1%
18973
 
9.2%
84885
 
5.0%
31768
 
1.8%
9888
 
0.9%
535
 
< 0.1%
717
 
< 0.1%
197
 
< 0.1%
135
 
< 0.1%
ValueCountFrequency (%)
045479
46.9%
18973
 
9.2%
235008
36.1%
31768
 
1.8%
535
 
< 0.1%
717
 
< 0.1%
84885
 
5.0%
9888
 
0.9%
135
 
< 0.1%
197
 
< 0.1%
ValueCountFrequency (%)
197
 
< 0.1%
135
 
< 0.1%
9888
 
0.9%
84885
 
5.0%
717
 
< 0.1%
535
 
< 0.1%
31768
 
1.8%
235008
36.1%
18973
 
9.2%
045479
46.9%

total_length_args
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct445
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.01870911
Minimum0
Maximum820
Zeros45479
Zeros (%)46.9%
Negative0
Negative (%)0.0%
Memory size758.4 KiB
2021-12-14T18:14:02.162504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12
Q360
95-th percentile241
Maximum820
Range820
Interquartile range (IQR)60

Descriptive statistics

Standard deviation84.38372716
Coefficient of variation (CV)1.622180339
Kurtosis5.166963568
Mean52.01870911
Median Absolute Deviation (MAD)12
Skewness2.074510917
Sum5049196
Variance7120.61341
MonotonicityNot monotonic
2021-12-14T18:14:02.418497image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
045479
46.9%
412296
 
2.4%
322140
 
2.2%
162120
 
2.2%
32057
 
2.1%
252023
 
2.1%
122015
 
2.1%
331409
 
1.5%
421377
 
1.4%
471060
 
1.1%
Other values (435)35089
36.2%
ValueCountFrequency (%)
045479
46.9%
32057
 
2.1%
4458
 
0.5%
539
 
< 0.1%
618
 
< 0.1%
774
 
0.1%
837
 
< 0.1%
1137
 
< 0.1%
122015
 
2.1%
13498
 
0.5%
ValueCountFrequency (%)
8201
 
< 0.1%
8191
 
< 0.1%
8111
 
< 0.1%
8102
< 0.1%
8082
< 0.1%
8061
 
< 0.1%
8041
 
< 0.1%
8032
< 0.1%
8011
 
< 0.1%
7993
< 0.1%

total_length_request
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct417
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.12705919
Minimum31
Maximum895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size758.4 KiB
2021-12-14T18:14:02.780502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile53
Q157
median59
Q372
95-th percentile307
Maximum895
Range864
Interquartile range (IQR)15

Descriptive statistics

Standard deviation70.24082847
Coefficient of variation (CV)0.815548901
Kurtosis11.60577026
Mean86.12705919
Median Absolute Deviation (MAD)5
Skewness3.191510744
Sum8359923
Variance4933.773984
MonotonicityNot monotonic
2021-12-14T18:14:03.094501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5717468
18.0%
567623
 
7.9%
587585
 
7.8%
597471
 
7.7%
536051
 
6.2%
665114
 
5.3%
674077
 
4.2%
613648
 
3.8%
602289
 
2.4%
712176
 
2.2%
Other values (407)33563
34.6%
ValueCountFrequency (%)
3152
 
0.1%
326
 
< 0.1%
34185
0.2%
3559
 
0.1%
3615
 
< 0.1%
385
 
< 0.1%
3952
 
0.1%
4098
0.1%
4125
 
< 0.1%
42236
0.2%
ValueCountFrequency (%)
8952
< 0.1%
8861
< 0.1%
8832
< 0.1%
8811
< 0.1%
8801
< 0.1%
8792
< 0.1%
8741
< 0.1%
8621
< 0.1%
8591
< 0.1%
8541
< 0.1%

lenght_of_path
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct65
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.32385515
Minimum22
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size758.4 KiB
2021-12-14T18:14:03.436502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile45
Q148
median49
Q352
95-th percentile59
Maximum88
Range66
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.154815582
Coefficient of variation (CV)0.1024328436
Kurtosis6.552216591
Mean50.32385515
Median Absolute Deviation (MAD)1
Skewness0.8085316652
Sum4884685
Variance26.57212369
MonotonicityNot monotonic
2021-12-14T18:14:03.849499image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4920830
21.5%
4816819
17.3%
5010949
11.3%
517471
 
7.7%
456051
 
6.2%
525650
 
5.8%
475441
 
5.6%
575092
 
5.2%
585077
 
5.2%
594077
 
4.2%
Other values (55)9608
9.9%
ValueCountFrequency (%)
2212
 
< 0.1%
2352
 
0.1%
246
 
< 0.1%
26185
0.2%
2759
 
0.1%
2810
 
< 0.1%
305
 
< 0.1%
3152
 
0.1%
3298
0.1%
3325
 
< 0.1%
ValueCountFrequency (%)
881
 
< 0.1%
878
 
< 0.1%
865
 
< 0.1%
853
 
< 0.1%
849
 
< 0.1%
8313
< 0.1%
8226
< 0.1%
8132
< 0.1%
8031
< 0.1%
7927
< 0.1%

port_is_common
Boolean

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size94.9 KiB
True
96668 
False
 
397
ValueCountFrequency (%)
True96668
99.6%
False397
 
0.4%
2021-12-14T18:14:04.023537image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

num_of_paths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.03023747
Minimum4
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size758.4 KiB
2021-12-14T18:14:04.187502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile7
Q17
median7
Q37
95-th percentile8
Maximum18
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4491382656
Coefficient of variation (CV)0.06388664217
Kurtosis72.11202673
Mean7.03023747
Median Absolute Deviation (MAD)0
Skewness1.910169501
Sum682390
Variance0.2017251816
MonotonicityNot monotonic
2021-12-14T18:14:04.353553image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
785079
87.7%
87291
 
7.5%
63202
 
3.3%
5813
 
0.8%
9420
 
0.4%
4214
 
0.2%
1029
 
< 0.1%
1817
 
< 0.1%
ValueCountFrequency (%)
4214
 
0.2%
5813
 
0.8%
63202
 
3.3%
785079
87.7%
87291
 
7.5%
9420
 
0.4%
1029
 
< 0.1%
1817
 
< 0.1%
ValueCountFrequency (%)
1817
 
< 0.1%
1029
 
< 0.1%
9420
 
0.4%
87291
 
7.5%
785079
87.7%
63202
 
3.3%
5813
 
0.8%
4214
 
0.2%

num_sql_words
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3893885541
Minimum0
Maximum5
Zeros73275
Zeros (%)75.5%
Negative0
Negative (%)0.0%
Memory size758.4 KiB
2021-12-14T18:14:04.529507image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7628178174
Coefficient of variation (CV)1.959014484
Kurtosis2.745747875
Mean0.3893885541
Median Absolute Deviation (MAD)0
Skewness1.92013924
Sum37796
Variance0.5818910225
MonotonicityNot monotonic
2021-12-14T18:14:04.738497image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
073275
75.5%
112436
 
12.8%
28744
 
9.0%
32569
 
2.6%
440
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
073275
75.5%
112436
 
12.8%
28744
 
9.0%
32569
 
2.6%
440
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
440
 
< 0.1%
32569
 
2.6%
28744
 
9.0%
112436
 
12.8%
073275
75.5%

num_batch_words
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size758.4 KiB
0
95340 
1
 
1596
2
 
124
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
095340
98.2%
11596
 
1.6%
2124
 
0.1%
35
 
< 0.1%

Length

2021-12-14T18:14:04.921498image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-14T18:14:05.079504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
095340
98.2%
11596
 
1.6%
2124
 
0.1%
35
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

num_special_chars
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct65
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.76937104
Minimum0
Maximum130
Zeros51051
Zeros (%)52.6%
Negative0
Negative (%)0.0%
Memory size758.4 KiB
2021-12-14T18:14:05.283501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile16
Maximum130
Range130
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.320669255
Coefficient of variation (CV)1.942146097
Kurtosis90.37716947
Mean3.76937104
Median Absolute Deviation (MAD)0
Skewness6.842379097
Sum365874
Variance53.59219834
MonotonicityNot monotonic
2021-12-14T18:14:05.668502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
051051
52.6%
48208
 
8.5%
16459
 
6.7%
35867
 
6.0%
72383
 
2.5%
52039
 
2.1%
131874
 
1.9%
121785
 
1.8%
91783
 
1.8%
141740
 
1.8%
Other values (55)13876
 
14.3%
ValueCountFrequency (%)
051051
52.6%
16459
 
6.7%
21611
 
1.7%
35867
 
6.0%
48208
 
8.5%
52039
 
2.1%
61395
 
1.4%
72383
 
2.5%
8988
 
1.0%
91783
 
1.8%
ValueCountFrequency (%)
1301
 
< 0.1%
1291
 
< 0.1%
1272
 
< 0.1%
1265
< 0.1%
1252
 
< 0.1%
1242
 
< 0.1%
1238
< 0.1%
1223
 
< 0.1%
1212
 
< 0.1%
1207
< 0.1%

x0_GET
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size758.4 KiB
1.0
71088 
0.0
25977 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.071088
73.2%
0.025977
 
26.8%

Length

2021-12-14T18:14:05.920538image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-14T18:14:06.055498image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.071088
73.2%
0.025977
 
26.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

x0_POST
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size758.4 KiB
0.0
71485 
1.0
25580 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.071485
73.6%
1.025580
 
26.4%

Length

2021-12-14T18:14:06.203501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-14T18:14:06.340501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.071485
73.6%
1.025580
 
26.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

x0_PUT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size758.4 KiB
0.0
96668 
1.0
 
397

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.096668
99.6%
1.0397
 
0.4%

Length

2021-12-14T18:14:06.474504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-14T18:14:06.632503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.096668
99.6%
1.0397
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

x1_localhost:8080
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size758.4 KiB
1.0
96668 
0.0
 
397

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.096668
99.6%
0.0397
 
0.4%

Length

2021-12-14T18:14:06.782500image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-14T18:14:06.953498image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.096668
99.6%
0.0397
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

x1_localhost:9090
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size758.4 KiB
0.0
96668 
1.0
 
397

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.096668
99.6%
1.0397
 
0.4%

Length

2021-12-14T18:14:07.117501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-14T18:14:07.262507image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.096668
99.6%
1.0397
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

x2_application/x-www-form-urlencoded
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size758.4 KiB
0.0
71088 
1.0
25977 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.071088
73.2%
1.025977
 
26.8%

Length

2021-12-14T18:14:07.398501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-14T18:14:07.541503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.071088
73.2%
1.025977
 
26.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

x2_nan
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size758.4 KiB
1.0
71088 
0.0
25977 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.071088
73.2%
0.025977
 
26.8%

Length

2021-12-14T18:14:07.735498image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-14T18:14:07.867502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.071088
73.2%
0.025977
 
26.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

x3_Mozilla/5.0
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size758.4 KiB
1.0
97065 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.097065
100.0%

Length

2021-12-14T18:14:08.004501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-14T18:14:08.153502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.097065
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

x4_KHTML/3.5.8 (like Gecko)
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size758.4 KiB
1.0
97065 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.097065
100.0%

Length

2021-12-14T18:14:08.289498image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-14T18:14:08.405499image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.097065
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

x5_HTTP/1.1
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size758.4 KiB
1.0
97065 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.097065
100.0%

Length

2021-12-14T18:14:08.565503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-14T18:14:08.698501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.097065
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

x6_compatible; Konqueror/3.5; Linux
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size758.4 KiB
1.0
97065 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.097065
100.0%

Length

2021-12-14T18:14:08.834500image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-14T18:14:08.952499image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.097065
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-12-14T18:13:53.276505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:23.047500image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:26.051507image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:29.065505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:32.983506image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:36.230503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:39.644501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:42.939502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:46.807505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:50.235503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:53.555500image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:23.374501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:26.447499image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:29.360505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:33.331504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:36.513503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:39.906502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:43.464503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:47.353503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:50.489500image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:53.872501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:23.716500image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:26.712501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:29.664503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:33.656504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:37.042499image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:40.191505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:43.777496image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:47.690496image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:50.805497image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:54.866508image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:23.999507image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:26.983505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:30.014499image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:34.080501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:37.505505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:40.473498image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:44.102504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:47.995502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:51.123497image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:55.190496image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:24.319501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:27.277506image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:30.392502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:34.375500image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:37.926501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:40.768502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:44.418508image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:48.342498image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:51.420502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:55.480505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:24.553503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:27.554503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:30.704501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:34.683501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:38.241497image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:41.115503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:44.719505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:48.625502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:51.737506image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:55.844537image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:24.825497image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:27.796503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:30.983502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:34.939504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:38.498502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:41.471503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:45.003504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:48.921505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:52.053503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:56.164504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:25.093500image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:28.078504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:31.813498image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:35.296497image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:38.803507image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:41.868503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:45.351501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:49.239497image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:52.361500image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:56.466500image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:25.358503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:28.412501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:32.141504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:35.616503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:39.097500image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:42.297503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:45.825501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:49.522499image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:52.683501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:56.776501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:25.668504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:28.751500image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:32.436501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:35.927499image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:39.379503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:42.653505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:46.463504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:49.894505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-12-14T18:13:52.963504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-12-14T18:14:09.715505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-14T18:14:10.435496image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-14T18:14:11.183500image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-14T18:14:11.937518image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-12-14T18:14:12.423505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-14T18:13:57.355499image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-14T18:13:58.711501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

anomalousContent-Lengthnum_of_argsmax_length_of_argsmin_length_of_argstotal_length_argstotal_length_requestlenght_of_pathport_is_commonnum_of_pathsnum_sql_wordsnum_batch_wordsnum_special_charsx0_GETx0_POSTx0_PUTx1_localhost:8080x1_localhost:9090x2_application/x-www-form-urlencodedx2_nanx3_Mozilla/5.0x4_KHTML/3.5.8 (like Gecko)x5_HTTP/1.1x6_compatible; Konqueror/3.5; Linux
0False000004840True60001.00.00.01.00.00.01.01.01.01.01.0
1False0102817413248True70091.00.00.01.00.00.01.01.01.01.01.0
2False7410191655749True70060.01.00.01.00.01.00.01.01.01.01.0
3False0101526012252True70031.00.00.01.00.00.01.01.01.01.01.0
4False601092516153True70000.01.00.01.00.01.00.01.01.01.01.0
5False02102127157True71031.00.00.01.00.00.01.01.01.01.01.0
6False422136658True71000.01.00.01.00.01.00.01.01.01.01.0
7False000005850True70001.00.00.01.00.00.01.01.01.01.01.0
8False02338419148True72041.00.00.01.00.00.01.01.01.01.01.0
9False332248325749True70010.01.00.01.00.01.00.01.01.01.01.0

Last rows

anomalousContent-Lengthnum_of_argsmax_length_of_argsmin_length_of_argstotal_length_argstotal_length_requestlenght_of_pathport_is_commonnum_of_pathsnum_sql_wordsnum_batch_wordsnum_special_charsx0_GETx0_POSTx0_PUTx1_localhost:8080x1_localhost:9090x2_application/x-www-form-urlencodedx2_nanx3_Mozilla/5.0x4_KHTML/3.5.8 (like Gecko)x5_HTTP/1.1x6_compatible; Konqueror/3.5; Linux
97055True254263122295749False700120.00.01.00.01.01.00.01.01.01.01.0
97056True02673230437949True720221.00.00.01.00.00.01.01.01.01.01.0
97057True320267322955850True700190.01.00.01.00.01.00.01.01.01.01.0
97058True02631224231749True720171.00.00.01.00.00.01.01.01.01.01.0
97059True258263122335850True700140.01.00.01.00.01.00.01.01.01.01.0
97060True02631223931449True720151.00.00.01.00.00.01.01.01.01.01.0
97061True255263122305850True700120.01.00.01.00.01.00.01.01.01.01.0
97062True000006254True70001.00.00.01.00.00.01.01.01.01.01.0
97063True000005446True80001.00.00.01.00.00.01.01.01.01.01.0
97064True000006961True70001.00.00.01.00.00.01.01.01.01.01.0

Duplicate rows

Most frequently occurring

anomalousContent-Lengthnum_of_argsmax_length_of_argsmin_length_of_argstotal_length_argstotal_length_requestlenght_of_pathport_is_commonnum_of_pathsnum_sql_wordsnum_batch_wordsnum_special_charsx0_GETx0_POSTx0_PUTx1_localhost:8080x1_localhost:9090x2_application/x-www-form-urlencodedx2_nanx3_Mozilla/5.0x4_KHTML/3.5.8 (like Gecko)x5_HTTP/1.1x6_compatible; Konqueror/3.5; Linux# duplicates
4False000005749True70001.00.00.01.00.00.01.01.01.01.01.08000
1False000005345True70001.00.00.01.00.00.01.01.01.01.01.06000
3False000005648True70001.00.00.01.00.00.01.01.01.01.01.04000
5False000005850True70001.00.00.01.00.00.01.01.01.01.01.04000
6False000005951True70001.00.00.01.00.00.01.01.01.01.01.04000
0False000004840True60001.00.00.01.00.00.01.01.01.01.01.02000
2False000005547True70001.00.00.01.00.00.01.01.01.01.01.02000
7False000006052True70001.00.00.01.00.00.01.01.01.01.01.02000
8False000006557True70001.00.00.01.00.00.01.01.01.01.01.02000
9False000006658True80001.00.00.01.00.00.01.01.01.01.01.02000